Benchmarking food marketing to youth in Canada: A scoping review
Bibliographic record
Abstract
Substantial international evidence has implicated food and beverage marketing as a potent driver behind childhood obesity. In Canada, there is a wide variety of evidence that has indicated the influential role food marketing plays on children’s dietary behaviours, yet there is no research that has comprehensively examined the totality of both peer-reviewed research and grey literature. This scoping review aims to identify and summarize all types of available and recent evidence regarding the exposure and impact of food marketing, as well as the influence of marketing regulations on Canadian youth. This review will include all peer-reviewed and grey literature from 2016 to present (2020). Study findings will be used to provide a benchmark of children’s exposure to and the frequency of food marketing and various marketing techniques across all media and settings, as well as to be used as a resource for policy and governmental priorities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.033 | 0.061 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".